PHD MARKETING COURSES

MKT 501. WORKSHOP IN MARKETING

This workshop provides a forum for the presentation of ongoing and completed research by students, faculty and visiting scholars. PhD students are expected to participate actively.

Prerequisite: permission of the instructor

MKT 511. ADVANCED TOPICS IN MARKETING I

This course is the first leg of a three-part sequence that prepares PhD students for research in marketing. The presentation of topics between the three parts may vary from year to year. The aim is to survey the literature, assess progress and identify opportunities for future research.

Prerequisite: permission of the instructor

MKT 512. ADVANCED TOPICS IN MARKETING II

In this second part of a three-part sequence that prepares PhD students for research in marketing, topics are discussed in a format similar to MKT 511.

Prerequisite: permission of the instructor

MKT 513. ADVANCED TOPICS IN MARKETING III

In this third part of a three-part sequence that prepares PhD students for research in marketing, topics are discussed in a format similar to MKT 511 and MKT 512.

Prerequisite: permission of the instructor

MKT 520. CAUSAL INFERENCE

The course will cover how to design compelling research, the focus of which is causal inference. The course covers the design of true experiments and concepts of validity (internal validity, external validity, replicability). The approach should follow the Rubin potential outcomes framework. The course then covers causal inference and related econometric methods in observational studies for cross-sectional, panel data, and time-series, and non-linear models including OLS, instrumental vriables, Heckman selection models, regression discontinuity designs, matched sample designs, granger causality, event studies, diff-in-diff, fixed effects, clustering standard errors, dynamic panel methods (e.g., Blundell and Bond 1998), and some issues in logit/probit/multinomial logit. Although the course will discuss many econometric techniques, students are expected to have already learned the mechanics of these methods, so that the course can focus on causal inference and its limitations in these methodologies.

AEC 523. MICRO-ECONOMETRIC MODELING: STATIC APPROACHES

This course introduces students to canonical modeling approaches for analyzying decision making by both firms and consumers, focusing on static environments. Central topics include demand estimation, models of strategic interaction, networks and platforms and auctions. Applications include firm pricing decisions, new product introductions, strategic entry and vertical relationships. The course generally includes coding assignments and student presentations, in addition to the weekly lectures on methods and applictions.

AEC 524. MICRO-ECONOMETRIC MODELING: DYNAMIC APPROACHES

This course examines consumer and firm behaviors that involve inter-temporal trade-offs and as a result involve dynamic optimization on the part of both consumers and firms. It begins with an overview of dynamic programming methods, in both single and multi-agent settings, emphasizing methods that link estimation with computation. Single agent topics include models of capital replacemrnt, dynamic demand, inventory models and salesforce management. Multi-agent topics include strategic innovation, learning by doing, demand smoothing, and product repositioning. A strong emphasis is placed on recent methods and frontier topics. The course generally includes coding assignments and several student presentations, in addition to weekly lectures.

MKT 505. MARKETING RESEARCH PHD WORKSHOP

This workshop provides a forum for the presentation of research ideas and completed research by students. The course includes discussion of current job market papers and AMA interviews, journal reviewing, and generating new research ideas. In addition, some topics are covered to illustrate current research areas of interest for the faculty. All marketing PhD students who are not on the job market are expected to participate actively.